Window function: lead/lag over category
Last updated: September 30, 2025
Quick Overview
Use window functions to compute running total partitioned by date.
Confluent
September 30, 202550
7
2,315 solved
Use window functions to compute running total partitioned by date.
Confluent asks this during the Onsite because data engineering skills are critical for the role. You should be comfortable with complex joins, window functions, CTEs, and performance optimization.
What the Interviewer Expects
- Solve complex analytical problems with elegant, readable SQL
- Optimize queries for large-scale datasets with partitioning and indexing
- Use recursive CTEs, lateral joins, and advanced window functions
- Design the data model alongside the query solution
- Discuss trade-offs between SQL and programmatic approaches (Python/pandas)
- Consider the operational aspects: query scheduling, incremental processing
Key Topics to Cover
How to Approach This
- Clarify the schema and expected output format before writing queries.
- Use CTEs (WITH clauses) to break complex queries into readable steps.
- Consider window functions (ROW_NUMBER, RANK, LAG, LEAD) for ranking and sequential analysis.
- Watch for NULLs, duplicates, and edge cases in JOINs and GROUP BY.
- For pandas, prefer vectorized operations over row-by-row iteration.
Possible Follow-up Questions
- What would you do if this query needs to run every 5 minutes?
- How would you handle slowly changing dimensions in this scenario?
- How would you handle this if the data was spread across multiple databases?
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Practice SQL ProblemsSample Answer
Approach
Break the problem into logical steps before writing SQL. Think about: 1. What tables do I need to join and on which keys? 2. What filtering (WHERE) d...
Solution Pattern
```sql WITH filtered_data AS ( SELECT * FROM main_table WHERE condition = 'value' AND date_col >= '2024-01-01' ), aggregated AS ( SELECT ...
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